Identification of Oils from Samara Region Using Principal Component Analysis and Factor Discriminant Analysis
Development of methods for identification of oils is of high priority in oil industry. The following parameters for 2963 oil samples from five oilfields in the Samara region were determined: density, fraction yield at 200 °C and 300 °C, the mass fraction of sulfur, hydrogen sulphide, methyl and ethyl mercaptan, the mass concentration of chloride salts, the saturated vapor pressure. The matrix of experi- mental data was analyzed using principal component analysis (PCA) and factorial discriminant analysis (FDA) methods. The models obtained are able to determine the oilfield of samples with prob- ability of almost 100%. Chemometric models have been proved by the independent test set validation, which showed the accuracy and stability of the models. The results of the analysis indicated the prospects of application of chemometric methods in the investigation of oil samples from Samara region and the developed approach can be used to discriminate oils from another regions.
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